SOKOLOVA E., DOROFEEV V. DEVELOPING ENGLISH LANGUAGE ABSTRACT WRITING SKILLS IN ENGINEERING STUDENTS THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES. LIFELONG EDUCATION: The 21st Century.
2026. Vol. 14. No. 3. DOI: 10.15393/j5.art.2026.13064


Vol. 14. No. 3.

Innovative approaches to lifelong learning
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DEVELOPING ENGLISH LANGUAGE ABSTRACT WRITING SKILLS IN ENGINEERING STUDENTS THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES

SOKOLOVA Evgeniya I.
PhD in Pedagogical Sciences, Associate Professor, Department of Foreign Languages of Natural and Technical Majors and Specialties
Petrozavodsk State University
(Petrozavodsk, Russian Federation)
esokolova@mail.ru
DOROFEEV Vladislav B.
Senior Lecturer, Department of Foreign Languages of Natural and Technical Majors and Specialties
Petrozavodsk State University
(Petrozavodsk, Russian Federation)
vladis.dorofeev2018@yandex.ru
Keywords:
artificial intelligence
academic writing
engineering students
abstract
ChatGPT
SCRIIB methodological model
pedagogical experiment
metacognitive strategies
cognitive dependence.		 Abstract: contemporary linguodidactics faces a growing contradiction between the objective need to develop engineering students' skills in writing English-language scientific abstracts and their uncritical, non-reflective use of generative neural networks, which entails a real risk of substituting genuine learning activity with cognitive offloading. This contradiction becomes particularly acute in the context of rapid educational digitalisation, where the widespread availability of AI powered tools ‒ primarily ChatGPT and similar large language models ‒ creates an illusion of competence among learners without requiring substantial intellectual effort on their part. The aim of this study is to provide theoretical and methodological substantiation, development, and experimental verification of the author's SCRIIB model (Structuring – Composing – Revising – Instructing – Iterating – Benchmarking), which integrates metacognitive strategies into the process of AI-assisted academic writing. The proposed model effectively mitigates the risks of cognitive dependence, fosters students' critical thinking abilities, and contributes to sustained improvements in the quality of academic abstracts produced by future engineers. Future research directions include the design of specialised AI-based educational interfaces, adaptation of the model to other academic writing genres (such as research articles and project descriptions), and longitudinal studies to assess the long-term sustainability of the acquired skills in graduates' real-world professional practice.
Paper submitted on: 06/27/2026; Accepted on: 08/17/2026; Published online on: 09/26/2026.

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DOI: http://dx.doi.org/10.15393/j5.art.2026.13064